Drug‐induced acanthosis nigricans: A systematic review and new classification
Bibliographic record
Abstract
Drug-induced acanthosis nigricans is an uncommon subtype of acanthosis nigricans and the data on this topic is not well understood by clinicians as it is presently limited in the literature. Previous reports of drug-induced acanthosis nigricans have simply consisted of a list of drugs possibly implicated in causing acanthosis nigricans. Several drugs listed are based on single case reports without biopsy confirmation, report of clearing on stopping the drug or reporting on whether acanthosis nigricans recurred with drug rechallenge. A comprehensive literature search was conducted using PubMed, EMBASE(Ovid), Cochrane Library, Scopus, and Web of Science electronic databases. The authors screened the initial result of the search strategy by title and abstract using the following inclusion criteria: eligible studies included those with patients who developed acanthosis nigricans secondary to a drug. This study is the first to comprehensively review the drugs that have been implicated in the development of acanthosis nigricans. A total of 38 studies were included in the systematic review, and a total of 13 acanthosis nigricans inducing drugs were identified. Nicotinic acid and insulin were the two most significant drugs that were reported to cause acanthosis nigricans. By using the results of this study, we created a revised classification system of drug-induced acanthosis nigricans which can be used as a concise framework for clinicians to refer to.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.039 | 0.029 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".